code: def test_classifier(self):
      categories = [
          "alt.atheism",
          "soc.religion.christian",
          "comp.graphics",
          "sci.med",
      ]
      from sklearn.datasets import fetch_20newsgroups
  
      train_b = fetch_20newsgroups(
          subset="train",
          categories=categories,
          shuffle=True,
          random_state=42,
      )
      test_b = fetch_20newsgroups(
          subset="test",
          categories=categories,
          shuffle=True,
          random_state=42,
      )
      print("size of training set: %s" % (len(train_b["data"])))
      print("size of validation set: %s" % (len(test_b["data"])))
      print("classes: %s" % (train_b.target_names))
      x_train = train_b.data
      y_train = train_b.target
      x_test = test_b.data
      y_test = test_b.target
      classes = train_b.target_names
  
      clf = snlp.Classifier()
      clf.fit(x_train, y_train, ctype="nbsvm")
      self.assertGreaterEqual(clf.evaluate(x_test, y_test), 0.93)
      test_doc = (
          "god christ jesus mother mary church sunday lord heaven amen"
      )
      self.assertEqual(clf.predict(test_doc), 3)
  
file_path: ktrain/tests/test_zzz_shallownlp.py
imports: import os
  from unittest import TestCase, main, skip
  import numpy
  import testenv
  from ktrain.text import shallownlp
label_desc: Use image node_id 1 for calling the TestShallowNLP obj's underlying member method code with example usage: obj.test_classifier() without return types